GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 2.1 GNGTS 2024 count: a refinement procedure is then applied to enhance information from the local source, thereby diminishing associated epistemic uncertainty. This step serves as the foundation for medium-scale simulations, maintaining a balance between comprehensiveness and detailed intricacies. The offshore profiles of these simulations furnish the input for a subsequent filtering process using a K-means algorithm, which groups scenarios into clusters sharing similar wave shape and amplitude profiles. The representatives of these clusters are then selected for the final phase: conducting high-resolution flood simulations to define hazard curves and maps at the local level. The method is tested and validated in the port area of Catania, thanks to the comparison with the already available results derived from massive simulations using high-performance computing. The results exhibit a good agreement between the proposed method and the exhaustive use of all scenarios in the ensemble. The new method is further applied to the port of Ravenna, where a high-resolution grid structure has been meticulously established. References Armaroli, C., Duo, E., and Viavattene, C. (2019). From Hazard to Consequences: Evaluation of Direct and Indirect Impacts of Flooding Along the Emilia-Romagna Coastline, Italy. Frontiers in Earth Science 7. Available at: https://www.frontiersin.org/articles/10.3389/feart.2019.00203 [Accessed June 28, 2023]. Basili, R., Tiberti, M. M., Kastelic, V., Romano, F., Piatanesi, A., Selva, J., et al. (2013). Integrating geologic fault data into tsunami hazard studies. Natural Hazards and Earth System Sciences 13, 1025–1050. doi: 10.5194/nhess-13-1025-2013. Basili, R., Brizuela, B., Herrero, A., Iqbal, S., Lorito, S., Maesano, F. E., et al. (2021). The Making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18). Front. Earth Sci. 8. doi: 10.3389/feart.2020.616594. Bazzurro, P., & Allin Cornell, C. (1999). Disaggregation of seismic hazard. Bulletin of the Seismological Society of America , 89 (2), 501-520. Behrens, J., Løvholt, F., Jalayer, F., Lorito, S., Salgado-Gálvez, M. A., Sørensen, M., et al. (2021). Probabilistic Tsunami Hazard and Risk Analysis: A Review of Research Gaps. Frontiers in Earth Science 9. Available at: https://www.frontiersin.org/articles/10.3389/feart.2021.628772 [Accessed June 28, 2023]. Davies, G., Weber, R., Wilson, K., and Cummins, P. (2022). From offshore to onshore probabilistic tsunami hazard assessment via efficient Monte Carlo sampling. Geophysical Journal International 230, 1630–1651. doi: 10.1093/gji/ggac140. De la Asunción, M., Castro, M. J., Fernández-Nieto, E. D., Mantas, J. M., Acosta, S. O., and González-Vida, J. M. (2013). Efficient GPU implementation of a two waves TVD-WAF method for the two-dimensional one layer shallow water system on structured meshes. Computers & Fluids 80, 441–452. doi: 10.1016/j.compfluid.2012.01.012. Gibbons, S. J., Lorito, S., Macías, J., Løvholt, F., Selva, J., Volpe, M., et al. (2020). Probabilistic Tsunami Hazard Analysis: High Performance Computing for Massive Scale Inundation